{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib \n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import warnings\n",
    "import numpy as np\n",
    "\n",
    "##解决图例中文乱码，设置参数\n",
    "matplotlib.rcParams['font.family']=['sans-serif']\n",
    "matplotlib.rcParams['font.sans-serif']=['Songti SC']#显示中文\n",
    "matplotlib.rcParams['font.serif']=['Songti SC']\n",
    "matplotlib.rcParams['axes.unicode_minus']=False #正常显示符号\n",
    "\n",
    "#屏蔽警告信息\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 基本散点图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='sepal_length', ylabel='sepal_width'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df=pd.read_csv('../seaborn-data/iris.csv')\n",
    "#fit_reg=True 显示回归线，line_kws回归线样式设置\n",
    "sns.regplot(x=df[\"sepal_length\"], y=df[\"sepal_width\"],marker=\"+\",fit_reg=True,line_kws={\"color\":\"r\",\"alpha\":0.7,\"lw\":5})\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 多组数据散点图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 使用 'hue' 表示图例\n",
    "sns.lmplot( x=\"sepal_length\", y=\"sepal_width\", data=df, fit_reg=False, hue='species', legend=False,markers=[\"o\", \"x\", \"1\"])\n",
    " \n",
    "# 移动图例位置\n",
    "plt.legend(loc='lower right')\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
